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Course Outline
Introduction to Huawei’s AI Ecosystem
- Overview of Ascend AI hardware, including the 310, 910, and 910B chips.
- Insights into MindSpore, CANN, and associated supporting tools.
- Understanding the AI development workflow, from training to deployment.
Understanding the CANN Toolkit
- Defining CANN and highlighting its significance.
- Overview of core components, including ATC, AscendCL, and operator libraries.
- The critical role of CANN within AI inference pipelines.
Getting Started with MindSpore and CANN
- Environment setup involving MindSpore, CANN, and Python.
- Training a basic model using MindSpore.
- Exporting and converting models via the ATC tool.
Running Inference on Ascend Devices
- Utilizing OM models with AscendCL or Python APIs.
- Performing basic input and output preprocessing.
- Validating model outputs for accuracy.
Working with Other Frameworks
- Overview of support for TensorFlow, PyTorch, and ONNX.
- Supported operators and known limitations.
- Demonstration of simple model conversion (e.g., from ONNX to OM format).
Exploring the CANN and MindSpore Developer Ecosystem
- Key resources, including documentation, GitHub repositories, and sample code.
- Overview of MindSpore Hub and the model zoo.
- Information on community forums, events, and support channels.
Summary and Next Steps
Requirements
- A fundamental understanding of machine learning and deep learning principles.
- Basic programming proficiency in Python.
- No prior exposure to CANN or Ascend hardware is necessary.
Target Audience
- Machine learning developers interested in exploring model deployment workflows.
- Students and researchers new to Huawei’s AI ecosystem.
- AI framework contributors and enthusiasts interested in model acceleration techniques.
7 Hours
